Branch-and-Bound Tensor Networks for Exact Ground-State Characterization

TL;DR

The paper introduces the Branch-and-Bound Tensor Network (BBTN) method, which combines branch-and-bound with tropical tensor networks to efficiently solve large-scale NP-hard problems like spin glasses and maximum independent set, surpassing existing solvers.

Contribution

The authors develop BBTN, a novel algorithm that extends tensor network methods with branch-and-bound, enabling exact solutions at scales previously considered infeasible.

Findings

Successfully computed ground-state counts for 64x64 spin glasses.

Solved maximum independent set problems on 100x100 graphs.

Reduced computational time from years to minutes for hard instances.

Abstract

Characterizing the ground-state properties of disordered systems, such as spin glasses and combinatorial optimization problems, is fundamental to science and engineering. However, computing exact ground states and counting their degeneracies are generally NP-hard and #P-hard problems, respectively, posing a formidable challenge for exact algorithms. Recently, Tensor Networks methods, which utilize high-dimensional linear algebra and achieve massive hardware parallelization, have emerged as a rapidly developing paradigm for efficiently solving these tasks. Despite their success, these methods are fundamentally constrained by the exponential growth of space complexity, which severely limits their scalability. To address this bottleneck, we introduce the Branch-and-Bound Tensor Network (BBTN) method, which seamlessly integrates the adaptive search framework of branch-and-bound with the efficient contraction of tropical tensor networks, significantly extending the reach of exact algorithms. We show that BBTN significantly surpasses existing state-of-the-art solvers, setting new benchmarks for exact computation. It pushes the boundaries of tractability to previously unreachable scales, enabling exact ground-state counting for $\pm J$ spin glasses up to $64 \times 64$ and solving Maximum Independent Set problems on King's subgraphs up to $100 \times 100$. For hard instances, BBTN dramatically reduces the computational cost of standard Tropical Tensor Networks, compressing years of runtime into minutes. Furthermore, it outperforms leading integer-programming solvers by over 30$\times$, establishing a versatile and scalable framework for solving hard problems in statistical physics and combinatorial optimization.